This qualitative study explored emotional responses of two white Dutch student teachers during a Critical Race Theory (CRT) based course. Following Plutchik's (2001) classification of 32 emotions, the analysis of their weekly diaries resulted in the identification of 16 emotions. In both diaries similar emotional responses were identified. However, the analysis did not reveal a straightforward path these students emotionally went through. The number and types of emotional responses, both comfortable and uncomfortable, fluctuated weekly and occurred simultaneously in various combinations. Even when similar emotional responses were identified, students connected differently to the course content. This could be explained by different starting points both students had when entering the course. The findings add to past work by identifying a variety and complexity of emotional responses of white student teachers during a CRT based course and can be used to create course conditions to prepare teachers for contributing to anti-racist education.
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Several studies found that classrooms' indoor environmental quality (IEQ) can positively influence in-class activities. Understanding and quantifying the combined effect of four indoor environmental parameters, namely indoor air quality and thermal, acoustic, and lighting conditions on people is essential to create an optimal IEQ. Accordingly, a systematic approach was developed to study the effect of multiple IEQ parameters simultaneously. Methods for measuring the IEQ and students' perceived IEQ, internal responses, and academic performance were derived from literature. Next, this systematic approach was tested in a pilot study during a regular academic course. The perceptions, internal responses, and short-term academic performance of participating students (n = 163) were measured. During the pilot study, the IEQ of the classrooms varied slightly. Significant associations (p < 0.05) were observed between these natural variations and students' perceptions of the thermal environment and indoor air quality. These perceptions were significantly associated with their physiological and cognitive responses (p < 0.05). Furthermore, students' perceived cognitive responses were associated with their short-term academic performance (p < 0.01). The observed associations confirm the construct validity of the systematic approach. However, its validity for investigating the influence of lighting remains to be determined.
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A decline in both student well-being and engagement were reported during the COVID-pandemic. Stressors and internal energy sources can co-exist or be both absent, which might cohere with different student needs. This study aimed to develop student profiles on emotional exhaustion and engagement, as well as examine how profiles relate to student participation, academic performance, and overall well-being. Survey-data from 1,460 Dutch higher education students were analyzed and resulted in a quadrant model containing four student profiles on engagement and emotional exhaustion scores. Semi-structured interviews with 13 students and 10 teaching staff members were conducted to validate and further describe the student profiles. The majority of the survey participants were disengaged-exhausted (48%) followed by engaged-exhausted students (29%). Overall, the engagedenergized students performed best academically and had the highest levels of well-being and participation, although engaged-exhausted students were more active in extracurricular activities. The engaged exhausted students also experienced the most pressure to succeed. The qualitative validation of the student profiles demonstrates that students and teachers recognize and associate the profiles with themselves or other students. Changes in the profiles are attributed to internal and external factors, suggesting that they are not fixed but can be influenced by various factors. The practical relevance of the quadrant model is acknowledged by students and teachers and they shared experiences and tips, with potential applications in recognizing students’ well-being and providing appropriate support. This study enriches our grasp of student engagement and well-being in higher education, providing valuable insights for educational practices.
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The corona pandemic has forced higher education (HE) institutes to transition to online learning, with subsequent implications for student wellbeing. Aims: This study explored influences on student wellbeing throughout the first wave of the corona crisis in the Netherlands by testing serial mediation models of the relationships between perceived academic stress, depression, resilience, and HE support.
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The purpose of this study was to provide insight into the interplay between student perceptions of competence-based assessment and student self-efficacy, and how this influences student learning outcomes. Results reveal that student perceptions of the form authenticity aspect and the quality feedback aspect of assessment do predict student self-efficacy, confirming the role of mastery experiences and social persuasions in enhancing student self-efficacy as stated by social cognitive theory. Findings do not confirm mastery experiences as being a stronger source of self-efficacy information than social persuasions. Study results confirm the predictive role of students’ self-efficacy on their competence outcomes. Mediation analysis results indicate that student’s perceptions of assessment have an indirect effect on student’s competence evaluation outcomes through student’s self-efficacy. Study findings highlight which assessment characteristics, positively influencing students’ learning, contribute to the effectiveness of competence-based education. Limitations of the study and directions for future research are indicated.
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Learning is all about feedback. Runners, for example, use apps like the RunKeeper. Research shows that apps like that enhance engagement and results. And people think it is fun. The essence being that the behavior of the runner is tracked and communicated back to the runner in a dashboard. We wondered if you can reach the same positive effect if you had a dashboard for Study-behaviour. For students. And what should you measure, track and communicate? We wondered if we could translate the Quantified Self Movement into a Quantified Student. So, together with students, professors and companies we started designing & building Quantified Student Apps. Apps that were measuring all kinds of study-behaviour related data. Things like Time On Campus, Time Online, Sleep, Exercise, Galvanic Skin Response, Study Results and so on. We developed tools to create study – information and prototyped the Apps with groups of student. At the same time we created a Big Data Lake and did a lot of Privacy research. The Big Difference between the Quantified Student Program and Learning Analytics is that we only present the data to the student. It is his/her data! It is his/her decision to act on it or not. The Quantified Student Apps are designed as a Big Mother never a Big Brother. The project has just started. But we already designed, created and learned a lot. 1. We designed and build for groups of prototypes for Study behavior Apps: a. Apps that measure sleep & exercise and compare it to study results, like MyRhytm; b. Apps that measure study hours and compare it to study results, like Nomi; c. Apps that measure group behavior and signal problems, like Groupmotion; d. Apps that measure on campus time and compare it with peers, like workhorse; 2. We researched student fysics to see if we could find his personal Cup-A-Soup-Moment (meaning, can we find by looking at his/her biometrics when the concentration levels dip?); 3. We created a Big Data lake with student data and Open Data and are looking for correlation and causality there. We already found some interesting patterns. In doing so we learned a lot. We learned it is often hard to acquire the right data. It is hard to create and App or a solution that is presenting the data in the right way and presents it in a form of actionable information. We learned that health trackers are still very inprecise. We learned about (and solved some) challenges surrounding privacy. Next year (2017) we will scale the most promising prototype, measure the effects, start a new researchproject and continu working on our data lake. Things will be interesting, and we will blog about it on www.quantifiedstudent.nl.
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Obtaining credits, studying for exams, attending classes, engaging with fellow students and lecturers, living alone or with others, and taking part in extra-curricular activities: there is a fair amount for students in higher education to take in. There are also numerous external factors — such as the COVID-19 pandemic and the changing labour and housing market — that affect students. However, students experience these situations differently and deal with them in different ways. How can we ensure that, notwithstanding these stress factors and differences, as many students as possible become and remain engaged and energised? Happier students tend to be more engaged and generally achieve better study results.1 That is why student well-being is also a widely researched and important topic. The search is on for measures to promote student well-being and success. Having a clear idea of how things are going for a student and what they need is a starting point. This booklet helps readers to identify different student profiles and understand what is needed to improve student success. We zoom in on two key aspects of student success: engagement and emotional exhaustion.
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Several studies found that classrooms' indoor environmental quality (IEQ) can positively influence in-class activities. Understanding and quantifying the combined effect of four indoor environmental parameters, namely indoor air quality and thermal, acoustic, and lighting conditions on people is essential to create an optimal IEQ. Accordingly, a systematic approach was developed to study the effect of multiple IEQ parameters simultaneously. Methods for measuring the IEQ and students' perceived IEQ, internal responses, and academic performance were derived from literature. Next, this systematic approach was tested in a pilot study during a regular academic course. The perceptions, internal responses, and short-term academic performance of participating students (n = 163) were measured. During the pilot study, the IEQ of the classrooms varied slightly. Significant associations (p < 0.05) were observed between these natural variations and students' perceptions of the thermal environment and indoor air quality. These perceptions were significantly associated with their physiological and cognitive responses (p < 0.05). Furthermore, students' perceived cognitive responses were associated with their short-term academic performance (p < 0.01). The observed associations confirm the construct validity of the systematic approach. However, its validity for investigating the influence of lighting remains to be determined.
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Recently, there has been an increased interest in the well-being of students in higher education. Despite the widespread consensus on the importance of student well-being, a clear definition continues to be lacking. This study qualitatively examined the student perspective on the topic through semi-structured interviews at a university of applied sciences in the Netherlands (n = 27). A major recurring theme was well-being as a balance in the interplay between efforts directed towards studies and life beyond studies. This method of perceiving well- being deviates from theoretical definitions. Students mentioned various factors that influence their well-being. Responses ranged from personal and university related factors to external factors beyond their educational institution. This study contributes to the body of knowledge on the well-being of students in higher education and provides suggestions for educational institutions, such as incorporating a holistic perspective on students and learning; and focus points for the development of policies and practices.
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The student well-being study of the Study Success Research Group has mapped out how students are doing. Among other things, they researched how healthy and engaged the students are and how they experience their study resources and personal energy sources, such as resilience and selfefficiency. The research shows that students predominantly think that they have a healthy lifestyle and consider themselves to be healthy. However, a large part of the students experiences stress on a regular basis (to a large extent) during their time as students. This infographic shows the top 10 of suggestions that students have for the students themselves, their teachers and for changes within education/the curriculum that could contribute to reducing stress and could promote the well-being of students.
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